{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cluster-GCN for node classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates how to use `StellarGraph`'s implementation of *Cluster-GCN*, [1], for node classification on a homogeneous graph.\n",
    "\n",
    "*Cluster-GCN* is an extension of the Graph Convolutional Network (GCN) algorithm, [2], for scalable training of deeper Graph Neural Networks using Stochastic Gradient Descent (SGD).\n",
    "\n",
    "As a first step, *Cluster-GCN* splits a given graph into `k` non-overlapping subgraphs, i.e., no two subgraphs share a node. In [1], it is suggested that for best classification performance, the *METIS* graph clustering algorithm, [3], should be utilised; *METIS* groups together nodes that form a well connected neighborhood with few connections to other subgraphs. The default clustering algorithm `StellarGraph` uses is the random assignment of nodes to clusters. The user is free to use any suitable clustering algorithm to determine the clusters before training the *Cluster-GCN* model. \n",
    "\n",
    "This notebook demonstrates how to use either random clustering or METIS. For the latter, it is necessary that 3rd party software has correctly been installed; later, we provide links to websites that host the software and provide detailed installation instructions. \n",
    "\n",
    "During model training, each subgraph or combination of subgraphs is treated as a mini-batch for estimating the parameters of a *GCN* model. A pass over all subgraphs is considered a training epoch.\n",
    "\n",
    "*Cluster-GCN* further extends *GCN* from the transductive to the inductive setting but this is not demonstrated in this notebook.\n",
    "\n",
    "This notebook demonstrates *Cluster-GCN* for node classification using 2 citation network datasets, `Cora` and `PubMed-Diabetes`.\n",
    "\n",
    "**References**\n",
    "\n",
    "[1] Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. W. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, and C. Hsiej, KDD, 2019, arXiv:1905.07953 ([download link](https://arxiv.org/abs/1905.07953))\n",
    "\n",
    "[2] Semi-Supervised Classification with Graph Convolutional Networks. T. Kipf, M. Welling. ICLR 2017. arXiv:1609.02907 ([download link](https://arxiv.org/abs/1609.02907))\n",
    "\n",
    "[3] METIS: Serial Graph Partitioning and Fill-reducing Matrix Ordering. ([download link](http://glaros.dtc.umn.edu/gkhome/views/metis))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import itertools\n",
    "import json\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from networkx.readwrite import json_graph\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import ClusterNodeGenerator\n",
    "from stellargraph.layer import ClusterGCN\n",
    "from stellargraph import globalvar\n",
    "\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "import warnings\n",
    "from scipy.sparse import SparseEfficiencyWarning\n",
    "\n",
    "warnings.simplefilter(\"ignore\", SparseEfficiencyWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_cora(data_dir, largest_cc=False):\n",
    "    g_nx = nx.read_edgelist(path=os.path.expanduser(os.path.join(data_dir, \"cora.cites\")))\n",
    "\n",
    "    for edge in g_nx.edges(data=True):\n",
    "        edge[2][\"label\"] = \"cites\"\n",
    "\n",
    "    # load the node attribute data\n",
    "    cora_data_location = os.path.expanduser(os.path.join(data_dir, \"cora.content\"))\n",
    "    node_attr = pd.read_csv(cora_data_location, sep=\"\\t\", header=None)\n",
    "    values = {str(row.tolist()[0]): row.tolist()[-1] for _, row in node_attr.iterrows()}\n",
    "    nx.set_node_attributes(g_nx, values, \"subject\")\n",
    "\n",
    "    if largest_cc:\n",
    "        # Select the largest connected component. For clarity we ignore isolated\n",
    "        # nodes and subgraphs; having these in the data does not prevent the\n",
    "        # algorithm from running and producing valid results.\n",
    "        g_nx_ccs = (g_nx.subgraph(c).copy() for c in nx.connected_components(g_nx))\n",
    "        g_nx = max(g_nx_ccs, key=len)\n",
    "        print(\n",
    "            \"Largest subgraph statistics: {} nodes, {} edges\".format(\n",
    "                g_nx.number_of_nodes(), g_nx.number_of_edges()\n",
    "            )\n",
    "        )\n",
    "\n",
    "    feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n",
    "    column_names = feature_names + [\"subject\"]\n",
    "    node_data = pd.read_csv(\n",
    "        os.path.join(data_dir, \"cora.content\"), sep=\"\\t\", header=None, names=column_names\n",
    "    )\n",
    "\n",
    "    node_data.index = node_data.index.map(str)\n",
    "    node_data = node_data[node_data.index.isin(list(g_nx.nodes()))]\n",
    "\n",
    "    for nid in node_data.index:\n",
    "        g_nx.nodes[nid][globalvar.TYPE_ATTR_NAME] = \"paper\"  # specify node type\n",
    "\n",
    "    return g_nx, node_data, feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pubmed(data_dir):\n",
    "    edgelist = pd.read_csv(\n",
    "        os.path.join(data_dir, \"Pubmed-Diabetes.DIRECTED.cites.tab\"),\n",
    "        sep=\"\\t\",\n",
    "        skiprows=2,\n",
    "        header=None,\n",
    "    )\n",
    "    edgelist.drop(columns=[0, 2], inplace=True)\n",
    "    edgelist.columns = [\"source\", \"target\"]\n",
    "    # delete unneccessary prefix\n",
    "    edgelist[\"source\"] = edgelist[\"source\"].map(lambda x: x.lstrip(\"paper:\"))\n",
    "    edgelist[\"target\"] = edgelist[\"target\"].map(lambda x: x.lstrip(\"paper:\"))\n",
    "    edgelist[\"label\"] = \"cites\"  # set the edge type\n",
    "\n",
    "    # Load the graph from the edgelist\n",
    "    g_nx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")\n",
    "\n",
    "    # Load the features and subject for each node in the graph\n",
    "    nodes_as_dict = []\n",
    "    with open(\n",
    "        os.path.join(os.path.expanduser(data_dir), \"Pubmed-Diabetes.NODE.paper.tab\")\n",
    "    ) as fp:\n",
    "        for line in itertools.islice(fp, 2, None):\n",
    "            line_res = line.split(\"\\t\")\n",
    "            pid = line_res[0]\n",
    "            feat_name = [\"pid\"] + [l.split(\"=\")[0] for l in line_res[1:]][\n",
    "                :-1\n",
    "            ]  # delete summary\n",
    "            feat_value = [l.split(\"=\")[1] for l in line_res[1:]][:-1]  # delete summary\n",
    "            feat_value = [pid] + [\n",
    "                float(x) for x in feat_value\n",
    "            ]  # change to numeric from str\n",
    "            row = dict(zip(feat_name, feat_value))\n",
    "            nodes_as_dict.append(row)\n",
    "\n",
    "    # Create a Pandas dataframe holding the node data\n",
    "    node_data = pd.DataFrame(nodes_as_dict)\n",
    "    node_data.fillna(0, inplace=True)\n",
    "    node_data[\"label\"] = node_data[\"label\"].astype(int)\n",
    "    node_data[\"label\"] = node_data[\"label\"].astype(str)\n",
    "\n",
    "    node_data.index = node_data[\"pid\"]\n",
    "    node_data.drop(columns=[\"pid\"], inplace=True)\n",
    "    node_data.head()\n",
    "\n",
    "    for nid in node_data.index:\n",
    "        g_nx.nodes[nid][globalvar.TYPE_ATTR_NAME] = \"paper\"  # specify node type\n",
    "\n",
    "    feature_names = list(node_data.columns)\n",
    "    feature_names.remove(\"label\")\n",
    "\n",
    "    return g_nx, node_data, feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the dataset\n",
    "\n",
    "This notebook demonstrates node classification using the *Cluster-GCN* algorithm using one of two citation networks, `Cora` and `Pubmed`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "The PubMed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(datasets.Cora().description))\n",
    "display(HTML(datasets.PubMedDiabetes().description))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = \"cora\"  # can also select 'pubmed'\n",
    "\n",
    "if dataset == \"cora\":\n",
    "    example_dataset = datasets.Cora()\n",
    "    example_dataset.download()\n",
    "    Gnx, node_data, feature_names = load_cora(example_dataset.data_directory)\n",
    "elif dataset == \"pubmed\":\n",
    "    example_dataset = datasets.PubMedDiabetes()\n",
    "    example_dataset.download()\n",
    "    Gnx, node_data, feature_names = load_pubmed(example_dataset.data_directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2708, 5278)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Gnx.number_of_nodes(), Gnx.number_of_edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "if dataset == \"cora\":\n",
    "    node_label = \"subject\"\n",
    "elif dataset == \"pubmed\":\n",
    "    node_label = \"label\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Case_Based',\n",
       " 'Genetic_Algorithms',\n",
       " 'Neural_Networks',\n",
       " 'Probabilistic_Methods',\n",
       " 'Reinforcement_Learning',\n",
       " 'Rule_Learning',\n",
       " 'Theory'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(node_data[node_label])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aim to train a graph-ML model that will predict the **subject** or **label** (depending on the dataset) attribute on the nodes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for validation and testing. We'll use scikit-learn again to do this.\n",
    "\n",
    "The number of labeled nodes we use for training depends on the dataset. We use 140 labeled nodes for `Cora` and 60 for `Pubmed` training. The validation and test sets have the same sizes for both datasets. We use 500 nodes for validation and the rest for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "if dataset == \"cora\":\n",
    "    train_size = 140\n",
    "elif dataset == \"pubmed\":\n",
    "    train_size = 60\n",
    "\n",
    "train_data, test_data = model_selection.train_test_split(\n",
    "    node_data, train_size=train_size, test_size=None, stratify=node_data[node_label]\n",
    ")\n",
    "val_data, test_data = model_selection.train_test_split(\n",
    "    test_data, train_size=500, test_size=None, stratify=test_data[node_label]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note using stratified sampling gives the following counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Theory': 18,\n",
       "         'Neural_Networks': 42,\n",
       "         'Case_Based': 16,\n",
       "         'Reinforcement_Learning': 11,\n",
       "         'Genetic_Algorithms': 22,\n",
       "         'Probabilistic_Methods': 22,\n",
       "         'Rule_Learning': 9})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "Counter(train_data[node_label])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_data[[node_label]].to_dict(\"records\"))\n",
    "val_targets = target_encoding.transform(val_data[[node_label]].to_dict(\"records\"))\n",
    "test_targets = target_encoding.transform(test_data[[node_label]].to_dict(\"records\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we prepare a Pandas DataFrame holding the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. `Cora` contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero. `Pubmed` has similar feature vectors associated with each node but the values are [tf-idf.](https://en.wikipedia.org/wiki/Tf%E2%80%93idf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_features = node_data[feature_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>w_0</th>\n",
       "      <th>w_1</th>\n",
       "      <th>w_2</th>\n",
       "      <th>w_3</th>\n",
       "      <th>w_4</th>\n",
       "      <th>w_5</th>\n",
       "      <th>w_6</th>\n",
       "      <th>w_7</th>\n",
       "      <th>w_8</th>\n",
       "      <th>w_9</th>\n",
       "      <th>...</th>\n",
       "      <th>w_1423</th>\n",
       "      <th>w_1424</th>\n",
       "      <th>w_1425</th>\n",
       "      <th>w_1426</th>\n",
       "      <th>w_1427</th>\n",
       "      <th>w_1428</th>\n",
       "      <th>w_1429</th>\n",
       "      <th>w_1430</th>\n",
       "      <th>w_1431</th>\n",
       "      <th>w_1432</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31336</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061127</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106406</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13195</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37879</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126012</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107140</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102850</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31349</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106418</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 1433 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         w_0  w_1  w_2  w_3  w_4  w_5  w_6  w_7  w_8  w_9  ...  w_1423  \\\n",
       "31336      0    0    0    0    0    0    0    0    0    0  ...       0   \n",
       "1061127    0    0    0    0    0    0    0    0    0    0  ...       0   \n",
       "1106406    0    0    0    0    0    0    0    0    0    0  ...       0   \n",
       "13195      0    0    0    0    0    0    0    0    0    0  ...       0   \n",
       "37879      0    0    0    0    0    0    0    0    0    0  ...       0   \n",
       "1126012    0    0    0    0    0    0    0    0    0    0  ...       0   \n",
       "1107140    0    0    0    0    0    0    0    0    0    0  ...       0   \n",
       "1102850    0    0    0    1    0    0    0    0    0    0  ...       0   \n",
       "31349      0    0    0    0    0    0    0    0    0    0  ...       0   \n",
       "1106418    0    0    0    0    0    0    0    0    0    0  ...       0   \n",
       "\n",
       "         w_1424  w_1425  w_1426  w_1427  w_1428  w_1429  w_1430  w_1431  \\\n",
       "31336         0       0       1       0       0       0       0       0   \n",
       "1061127       0       1       0       0       0       0       0       0   \n",
       "1106406       0       0       0       0       0       0       0       0   \n",
       "13195         0       0       0       0       0       0       0       0   \n",
       "37879         0       0       0       0       0       0       0       0   \n",
       "1126012       0       0       1       0       0       0       0       0   \n",
       "1107140       0       0       0       0       0       0       0       0   \n",
       "1102850       0       0       0       0       0       0       0       0   \n",
       "31349         0       0       0       0       0       0       0       0   \n",
       "1106418       0       0       0       0       0       0       0       0   \n",
       "\n",
       "         w_1432  \n",
       "31336         0  \n",
       "1061127       0  \n",
       "1106406       0  \n",
       "13195         0  \n",
       "37879         0  \n",
       "1126012       0  \n",
       "1107140       0  \n",
       "1102850       0  \n",
       "31349         0  \n",
       "1106418       0  \n",
       "\n",
       "[10 rows x 1433 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's have a look at the first 10 rows of the node features dataframe.\n",
    "node_features.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# drop the subject/label column\n",
    "train_data.drop([node_label], inplace=True, axis=1)\n",
    "test_data.drop([node_label], inplace=True, axis=1)\n",
    "val_data.drop([node_label], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train using cluster GCN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a `StellarGraph` object from the `NetworkX` graph and the node features and targets. It is the `StellarGraph` object that we use in this library to perform machine learning tasks on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = sg.StellarGraph(Gnx, node_features=node_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetworkXStellarGraph: Undirected multigraph\n",
      " Nodes: 2708, Edges: 5278\n",
      "\n",
      " Node types:\n",
      "  paper: [2708]\n",
      "        Attributes: {'subject'}\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [5278]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph Clustering \n",
    "\n",
    "*Cluster-GCN* requires that a graph is clustered into `k` non-overlapping subgraphs. These subgraphs are used as batches to train a *GCN* model. \n",
    "\n",
    "Any graph clustering method can be used, including random clustering that is the default clustering method in `StellarGraph`. \n",
    "\n",
    "However, the choice of clustering algorithm can have a large impact on performance. In the *Cluster-GCN* paper, [1], it is suggested that the *METIS* algorithm is used as it produces subgraphs that are well connected with few intra-graph edges. \n",
    "\n",
    "This demo uses random clustering by default. \n",
    "\n",
    "#### METIS\n",
    "\n",
    "In order to use *METIS*, you must download and install the official implemention from [here](http://glaros.dtc.umn.edu/gkhome/views/metis). Also, you must install the Python `metis` library by following the instructions [here.](https://metis.readthedocs.io/en/latest/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_clusters = 10  # the number of clusters/subgraphs\n",
    "clusters_per_batch = 2  # combine two cluster per batch\n",
    "random_clusters = True  # Set to False if you want to use METIS for clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_ids = np.array(G.nodes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "if random_clusters:\n",
    "    # We don't have to specify the cluster because the CluserNodeGenerator will take\n",
    "    # care of the random clustering for us.\n",
    "    clusters = number_of_clusters\n",
    "else:\n",
    "    import metis\n",
    "\n",
    "    # We are going to use the METIS clustering algorith,\n",
    "    print(\"Graph clustering using the METIS algorithm.\")\n",
    "    edgecuts, parts = metis.part_graph(G, number_of_clusters)\n",
    "    parts = np.array(parts)\n",
    "    clusters = []\n",
    "    cluster_ids = np.unique(parts)\n",
    "    for cluster_id in cluster_ids:\n",
    "        mask = np.where(parts == cluster_id)\n",
    "        clusters.append(node_ids[mask])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we create the `ClusterNodeGenerator` object that will give us access to a generator suitable for model training, evaluation, and prediction via the Keras API. \n",
    "\n",
    "We specify the number of clusters and the number of clusters to combine per batch, **q**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clusters 10\n",
      "0 cluster has size 270\n",
      "1 cluster has size 270\n",
      "2 cluster has size 270\n",
      "3 cluster has size 270\n",
      "4 cluster has size 270\n",
      "5 cluster has size 270\n",
      "6 cluster has size 270\n",
      "7 cluster has size 270\n",
      "8 cluster has size 270\n",
      "9 cluster has size 278\n"
     ]
    }
   ],
   "source": [
    "generator = ClusterNodeGenerator(G, clusters=clusters, q=clusters_per_batch, lam=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model, we need a few more parameters for this:\n",
    "\n",
    " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use two GCN layers with 32-dimensional hidden node features at each layer.\n",
    " * `activations` is a list of activations applied to each layer's output\n",
    " * `dropout=0.5` specifies a 50% dropout at each layer. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create the *Cluster-GCN* model as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cluster_gcn = ClusterGCN(\n",
    "    layer_sizes=[32, 32], activations=[\"relu\", \"relu\"], generator=generator, dropout=0.5\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a Keras model we now expose the input and output tensors of the *Cluster-GCN* model for node prediction, via the `ClusterGCN.build` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp, x_out = cluster_gcn.build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor 'input_1:0' shape=(1, None, 1433) dtype=float32>,\n",
       " <tf.Tensor 'input_2:0' shape=(1, None, None) dtype=int32>,\n",
       " <tf.Tensor 'input_3:0' shape=(1, None, None) dtype=float32>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_inp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'cluster_graph_convolution_1/Identity:0' shape=(None, None, 32) dtype=float32>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are also going to add a final layer dense layer with softmax output activation. This layers performs classification so we set the number of units to equal the number of classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'dense/Identity:0' shape=(None, None, 7) dtype=float32>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we build the Tensorflow model and compile it specifying the loss function, optimiser, and metrics to monitor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=x_inp, outputs=predictions)\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.01),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[\"acc\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to train the `ClusterGCN` model, keeping track of its loss and accuracy on the training set, and its generalisation performance on a validation set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need two generators, one for training and one for validation data. We can create such generators by calling the `flow` method of the `ClusterNodeGenerator` object we created earlier and specifying the node IDs and corresponding ground truth target values for each of the two datasets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_data.index, train_targets, name=\"train\")\n",
    "val_gen = generator.flow(val_data.index, val_targets, name=\"val\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we are ready to train our `ClusterGCN` model by calling the `fit_generator` method of our Tensorflow Keras model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "5/5 [==============================] - 1s 102ms/step - loss: 1.9188 - acc: 0.2357 - val_loss: 1.8709 - val_acc: 0.3020\n",
      "Epoch 2/20\n",
      "5/5 [==============================] - 0s 78ms/step - loss: 1.7970 - acc: 0.3000 - val_loss: 1.7884 - val_acc: 0.3020\n",
      "Epoch 3/20\n",
      "5/5 [==============================] - 0s 80ms/step - loss: 1.6586 - acc: 0.3000 - val_loss: 1.7209 - val_acc: 0.3020\n",
      "Epoch 4/20\n",
      "5/5 [==============================] - 0s 78ms/step - loss: 1.5054 - acc: 0.3286 - val_loss: 1.6128 - val_acc: 0.3760\n",
      "Epoch 5/20\n",
      "5/5 [==============================] - 0s 78ms/step - loss: 1.3468 - acc: 0.5357 - val_loss: 1.4935 - val_acc: 0.5380\n",
      "Epoch 6/20\n",
      "5/5 [==============================] - 0s 83ms/step - loss: 1.1142 - acc: 0.6714 - val_loss: 1.3682 - val_acc: 0.5540\n",
      "Epoch 7/20\n",
      "5/5 [==============================] - 0s 98ms/step - loss: 0.9295 - acc: 0.6643 - val_loss: 1.2803 - val_acc: 0.5800\n",
      "Epoch 8/20\n",
      "5/5 [==============================] - 0s 82ms/step - loss: 0.7081 - acc: 0.8000 - val_loss: 1.2349 - val_acc: 0.6040\n",
      "Epoch 9/20\n",
      "5/5 [==============================] - 0s 83ms/step - loss: 0.5858 - acc: 0.8000 - val_loss: 1.2381 - val_acc: 0.6080\n",
      "Epoch 10/20\n",
      "5/5 [==============================] - 0s 83ms/step - loss: 0.6231 - acc: 0.7571 - val_loss: 1.2813 - val_acc: 0.5940\n",
      "Epoch 11/20\n",
      "5/5 [==============================] - 0s 96ms/step - loss: 0.5166 - acc: 0.8000 - val_loss: 1.2886 - val_acc: 0.5960\n",
      "Epoch 12/20\n",
      "5/5 [==============================] - 0s 98ms/step - loss: 0.4260 - acc: 0.8500 - val_loss: 1.3381 - val_acc: 0.5660\n",
      "Epoch 13/20\n",
      "5/5 [==============================] - 0s 98ms/step - loss: 0.3986 - acc: 0.8357 - val_loss: 1.3102 - val_acc: 0.5800\n",
      "Epoch 14/20\n",
      "5/5 [==============================] - 0s 85ms/step - loss: 0.4797 - acc: 0.8357 - val_loss: 1.3130 - val_acc: 0.6020\n",
      "Epoch 15/20\n",
      "5/5 [==============================] - 1s 101ms/step - loss: 0.3126 - acc: 0.8857 - val_loss: 1.3098 - val_acc: 0.6120\n",
      "Epoch 16/20\n",
      "5/5 [==============================] - 0s 99ms/step - loss: 0.2641 - acc: 0.9286 - val_loss: 1.3429 - val_acc: 0.6000\n",
      "Epoch 17/20\n",
      "5/5 [==============================] - 0s 98ms/step - loss: 0.2804 - acc: 0.9143 - val_loss: 1.3547 - val_acc: 0.6060\n",
      "Epoch 18/20\n",
      "5/5 [==============================] - 0s 87ms/step - loss: 0.1982 - acc: 0.9214 - val_loss: 1.3093 - val_acc: 0.6160\n",
      "Epoch 19/20\n",
      "5/5 [==============================] - 0s 78ms/step - loss: 0.1979 - acc: 0.9429 - val_loss: 1.3158 - val_acc: 0.6220\n",
      "Epoch 20/20\n",
      "5/5 [==============================] - 0s 88ms/step - loss: 0.1906 - acc: 0.9071 - val_loss: 1.2742 - val_acc: 0.6240\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "    train_gen, validation_data=val_gen, epochs=20, verbose=1, shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the training history:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def remove_prefix(text, prefix):\n",
    "    return text[text.startswith(prefix) and len(prefix):]\n",
    "\n",
    "def plot_history(history):\n",
    "    metrics = sorted(set([remove_prefix(m, \"val_\") for m in list(history.history.keys())]))\n",
    "    for m in metrics:\n",
    "        # summarize history for metric m\n",
    "        plt.plot(history.history[m])\n",
    "        if history.history.get('val_'+m, None):\n",
    "            plt.plot(history.history['val_' + m])\n",
    "        plt.title(m)\n",
    "        plt.ylabel(m)\n",
    "        plt.xlabel('epoch')\n",
    "        plt.legend(['train', 'validation'], loc='best')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the best model on the test set.\n",
    "\n",
    "Note that *Cluster-GCN* performance can be very poor if using random graph clustering. Using *METIS* instead of random graph clustering produces considerably better results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gen = generator.flow(test_data.index, test_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 1.1680\n",
      "\tacc: 0.6601\n"
     ]
    }
   ],
   "source": [
    "test_metrics = model.evaluate_generator(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making predictions with the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For predictions to work correctly, we need to remove the extra batch dimensions necessary for the implementation of *Cluster-GCN* to work. We can easily achieve this by adding a layer after the dense predictions layer to remove this extra dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_flat = layers.Lambda(lambda x: K.squeeze(x, 0))(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor 'dense/Identity:0' shape=(None, None, 7) dtype=float32>,\n",
       " <tf.Tensor 'lambda_1/Identity:0' shape=(None, 7) dtype=float32>)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notice that we have removed the first dimension\n",
    "predictions, predictions_flat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get the predictions for all nodes.\n",
    "\n",
    "We need to create a new model using the same as before input Tensor and our new **predictions_flat** Tensor as the output. We are going to re-use the trained model weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_predict = Model(inputs=x_inp, outputs=predictions_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_nodes = list(node_data.index)\n",
    "all_gen = generator.flow(all_nodes, name=\"all_gen\")\n",
    "all_predictions = model_predict.predict_generator(all_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2708, 7)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_predictions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_predictions = target_encoding.inverse_transform(all_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at a few predictions after training the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2708"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_gen.node_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000012</th>\n",
       "      <td>subject=Rule_Learning</td>\n",
       "      <td>Rule_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100197</th>\n",
       "      <td>subject=Probabilistic_Methods</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100701</th>\n",
       "      <td>subject=Case_Based</td>\n",
       "      <td>Case_Based</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100935</th>\n",
       "      <td>subject=Genetic_Algorithms</td>\n",
       "      <td>Genetic_Algorithms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100961</th>\n",
       "      <td>subject=Neural_Networks</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101143</th>\n",
       "      <td>subject=Case_Based</td>\n",
       "      <td>Case_Based</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101145</th>\n",
       "      <td>subject=Genetic_Algorithms</td>\n",
       "      <td>Case_Based</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101261</th>\n",
       "      <td>subject=Theory</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101263</th>\n",
       "      <td>subject=Theory</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101660</th>\n",
       "      <td>subject=Rule_Learning</td>\n",
       "      <td>Rule_Learning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Predicted                True\n",
       "1000012          subject=Rule_Learning       Rule_Learning\n",
       "100197   subject=Probabilistic_Methods     Neural_Networks\n",
       "100701              subject=Case_Based          Case_Based\n",
       "100935      subject=Genetic_Algorithms  Genetic_Algorithms\n",
       "100961         subject=Neural_Networks     Neural_Networks\n",
       "101143              subject=Case_Based          Case_Based\n",
       "101145      subject=Genetic_Algorithms          Case_Based\n",
       "101261                  subject=Theory              Theory\n",
       "101263                  subject=Theory              Theory\n",
       "101660           subject=Rule_Learning       Rule_Learning"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(node_predictions, index=all_gen.node_order).idxmax(axis=1)\n",
    "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data[node_label]})\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node embeddings\n",
    "\n",
    "Evaluate node embeddings as activations of the output of the last graph convolution layer in the `ClusterGCN` layer stack and visualise them, coloring nodes by their true subject label. We expect to see nice clusters of papers in the node embedding space, with papers of the same subject belonging to the same cluster.\n",
    "\n",
    "To calculate the node embeddings rather than the class predictions, we create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings `x_out` rather than the predicted class. Additionally note that the weights trained previously are kept in the new model.\n",
    "\n",
    "Note that the embeddings from the `ClusterGCN` model have a batch dimension of 1 so we `squeeze` this to get a matrix of $N_{nodes} \\times N_{emb}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_out_flat = layers.Lambda(lambda x: K.squeeze(x, 0))(x_out)\n",
    "embedding_model = Model(inputs=x_inp, outputs=x_out_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 33ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2708, 32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb = embedding_model.predict_generator(all_gen, verbose=1)\n",
    "emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their true subject label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prediction Node Order**\n",
    "\n",
    "The predictions are not returned in the same order as the input nodes given. The generator object internally maintains the order of predictions. These are stored in the object's member variable `node_order`. We use `node_order` to re-index the `node_data` DataFrame such that the prediction order in `y` corresponds to that of node embeddings in `X`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = emb\n",
    "y = np.argmax(\n",
    "    target_encoding.transform(\n",
    "        node_data[[node_label]].reindex(index=all_gen.node_order).to_dict(\"records\")\n",
    "    ),\n",
    "    axis=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "if X.shape[1] > 2:\n",
    "    transform = TSNE  # or use PCA for speed\n",
    "\n",
    "    trans = transform(n_components=2)\n",
    "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=all_gen.node_order)\n",
    "    emb_transformed[\"label\"] = y\n",
    "else:\n",
    "    emb_transformed = pd.DataFrame(X, index=list(G.nodes()))\n",
    "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
    "    emb_transformed[\"label\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.scatter(\n",
    "    emb_transformed[0],\n",
    "    emb_transformed[1],\n",
    "    c=emb_transformed[\"label\"].astype(\"category\"),\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\n",
    "    \"{} visualization of GCN embeddings for cora dataset\".format(transform.__name__)\n",
    ")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
